Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI
As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology's capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems. Sociotechnical analysis of risks in highly complex systems provides clear lessons for the design and evaluation of AI systems, transcending a technical focus on reliable or "responsibly designed" components to understand risks at a systems level. Human-made catastrophes have been studied for decades because of the severity of these events: consider Chernobyl, Three Mile Island, Fukushima-Daiichi, Bhopal, the Challenger disaster. A co...
| Authors | Joshua A. Kroll, Andrew Smart, R. Stuart Geiger et al. |
|---|---|
| Primary category | cs.CY |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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